Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.
@article{arxiv.1806.04441,
title = {Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation},
author = {Haoyang Wen and Yijia Liu and Wanxiang Che and Libo Qin and Ting Liu},
journal= {arXiv preprint arXiv:1806.04441},
year = {2018}
}